tf.compat.v1.train.inverse_time_decay

When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an inverse decay function
to a provided initial learning rate. It requires an global_step value to
compute the decayed learning rate. You can just pass a TensorFlow variable
that you increment at each training step.

Args:

learning_rate: A scalar float32 or float64Tensor or a Python number.
The initial learning rate.

global_step: A Python number. Global step to use for the decay computation.
Must not be negative.

decay_steps: How often to apply decay.

decay_rate: A Python number. The decay rate.

staircase: Whether to apply decay in a discrete staircase, as opposed to
continuous, fashion.

name: String. Optional name of the operation. Defaults to
'InverseTimeDecay'.

Returns:

A scalar Tensor of the same type as learning_rate. The decayed
learning rate.

Raises:

ValueError: if global_step is not supplied.

Eager Compatibility

When eager execution is enabled, this function returns a function which in
turn returns the decayed learning rate Tensor. This can be useful for changing
the learning rate value across different invocations of optimizer functions.